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Effect of classroom air quality on students' concentration: Results of a cluster-randomized cross-over experimental study


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Abstract To assess the effect of indoor air quality as indicated by the median carbon dioxide (CO2) level in the classroom on the concentration performance (CP) of students, a cross-over cluster-randomized experimental study was conducted in 20 classrooms with mechanical ventilation systems. Test conditions ‘worse’ (median CO2 level on average 2115 ppm) and ‘better’ (median CO2 level on average 1045 ppm) were established by the regulation of the mechanical ventilation system on two days in one week each in every classroom. Concentration performance was quantified in students of grade three and four by the use of the d2-test and its primary parameter ‘CP’ and secondary parameters ‘total number of characters processed’ (TN) and ‘total number of errors’ (TE). 2366 d2-tests from 417 students could be used in analysis. In hierarchical linear regression accounting for repeated measurements, no significant effect of the experimental condition on CP or TN could be observed. However, TE was increased significantly by 1.65 (95% confidence interval 0.42–2.87) in ‘worse’ compared to ‘better’ condition. Thus, low air quality in classrooms as indicated by increased CO2 levels does not reduce overall short-term CP in students, but appears to increase the error rate. This study could not confirm that low air quality in classrooms as indicated by increased CO2 levels reduces short-term concentration performance (CP) in students; however, it appears to affect processing accuracy negatively. To ensure a high level of accuracy, good air quality characterized, for example, by low CO2 concentration should be maintained in classrooms.
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Effect of classroom air quality on studentsÕconcentration: results of
a cluster-randomized cross-over experimental study
In recent years, indoor environments in schools have
come into the focus of discussion. In particular, the
impact of indoor air quality on the attention and CP,
achievements, well-being, and health of students has
been discussed (Daisey et al., 2003; Haverinen-Shaugh-
nessy et al., 2011; Mendell and Heath, 2005; Shendell
et al., 2004).
Carbon dioxide (CO
) has been commonly used as
an indicator of indoor air quality. According to The
German Working Group on Indoor Guidelines of the
Federal Environment Agency and the StatesÕHealth
Authorities, air quality can be regarded as ÔharmlessÕif
levels are below 1000 ppm, ÔelevatedÕif between
1000 and 2000 ppm, and Ôhygienically unacceptableÕif
above 2000 ppm (Lahrz et al., 2008). This is in line
with guidelines from other European countries
(BMLFUW, 2006; UK Department of Education,
2006; NO-Folkehelseinstituttet 1996).
However, particularly in wintertime, increased CO
levels have been observed in classrooms. In a Bavarian
measurement campaign in 91 classrooms, median CO
levels ranged between 598 and 4172 ppm (Fromme
et al., 2008). In 25% of the classrooms, the median CO
level exceeded 2000 ppm and in 10%, 2700 ppm. Most
Abstract To assess the effect of indoor air quality as indicated by the median
carbon dioxide (CO
) level in the classroom on the concentration performance
(CP) of students, a cross-over cluster-randomized experimental study was con-
ducted in 20 classrooms with mechanical ventilation systems. Test conditions
ÔworseÕ(median CO
level on average 2115 ppm) and ÔbetterÕ(median CO
on average 1045 ppm) were established by the regulation of the mechanical
ventilation system on two days in one week each in every classroom. Concen-
tration performance was quantified in students of grade three and four by the
use of the d2-test and its primary parameter ÔCPÕand secondary parameters Ôtotal
number of characters processedÕ(TN) and Ôtotal number of errorsÕ(TE). 2366
d2-tests from 417 students could be used in analysis. In hierarchical linear
regression accounting for repeated measurements, no significant effect of the
experimental condition on CP or TN could be observed. However, TE was
increased significantly by 1.65 (95% confidence interval 0.42–2.87) in ÔworseÕ
compared to ÔbetterÕcondition. Thus, low air quality in classrooms as indicated
by increased CO
levels does not reduce overall short-term CP in students, but
appears to increase the error rate.
D. Twardella
, W. Matzen
T. Lahrz
, R. Burghardt
H. Spegel
, L. Hendrowarsito
A. C. Frenzel
, H. Fromme
Department of Occupational and Environmental
Health, Bavarian Health and Food Safety Authority,
Department of Toxicology and Chemical
Safety, Bavarian Health and Food Safety Authority,
Berlin-Brandenburg State Laboratory,
Department of Environmental Health Protection, Berlin,
Department of Psychology, University of Augsburg,
Augsburg, Germany
Key words: Attention; Carbon Dioxide; Indoor Air;
Students; School.
D. Twardella
Department of Occupational and Environmental Health
Bavarian Health and Food Safety Authority
Pfarrstrasse 3, 80538 Munich
Tel.: ++49-9131-6808 4249
Fax: ++49-9131-6808 4297
Received for review 26 October 2011. Accepted for
publication 15 February 2012.
Practical Implications
This study could not confirm that low air quality in classrooms as indicated by increased CO
levels reduces short-term
concentration performance (CP) in students; however, it appears to affect processing accuracy negatively. To ensure a
high level of accuracy, good air quality characterized, for example, by low CO
concentration should be maintained in
Indoor Air 2012; 22: 378–387
Printed in Singapore. All rights reserved
2012 John Wiley & Sons A/S
classrooms rely on natural ventilation, and the cold
temperature of outdoor air inhibits frequent window
opening and causes accumulation of CO
in indoor air.
High CO
levels in schools have been reported also from
other countries (Daisey et al., 2003).
The relevance of air quality as indicated by CO
in the classroom for the attention and concentration of
the students could not be consistently shown yet. In a
literature review from 2005, one single publication was
identified, which analyzed the performance depending
on indoor air quality (Mendell and Heath, 2005). A
negative effect of low air quality on reaction time and
performance was reported. More recent publications
provide further indications for an effect of air quality on
attention and concentration (Coley et al., 2007; Ribic,
2008; Wargocki and Wyon, 2007a,b). However, results
are not sufficiently conclusive yet, because multiple
testing as well as lack in blinding may have biased
observed associations. On this background, we initiated
the hereby reported RaBe (Raumluftqualita
¨t und das
Befinden von Kindern – Indoor air quality and student
experiences) study. The objective of the RaBe study was
to assess the effect of indoor air quality as indicated by
the CO
level in the classroom on the CP of students.
We conducted an experimental study with a cluster-
randomized cross-over design. Data collection took
place between November 2009 and April 2010. The
RaBe study was approved by the Ethics Board of
the Bavarian Chamber of Physicians, Germany and by
the Bavarian Ministry of Education and Culture.
Classes of grade three and four (children usually aged
9–10 years) with a mechanical ventilation system in the
classroom were eligible for participation. Primary
schools in the German states, Bavaria and Berlin/
Brandenburg, were approached by mail as well as
personally, and consent for participation was obtained
from the schools headmasters. Within each class,
parents were informed about the study aims and
procedures by written material as well as by an
information evening in each school, and consent for
data collection was obtained from parents of each
student. All students of the identified classes were
eligible for participation.
We included six schools in our study, five of which
were located in the State of Bavaria and one close to
Berlin. Most of the schools had been recently reno-
vated or built. Per school two to six classes participated
in the study. The classes consisted of 16–29 students of
which between 67% and 100% agreed to participate.
Overall, 417 students from 20 classes took part in the
study (overall response rate for students 84%).
Experimental conditions
In each classroom, three experimental conditions were
ÔUsualÕ: The mechanical ventilation was adjusted as
usual. Window opening was allowed according to
schools general regulations.
ÔWorseÕ: The mechanical ventilation was down-
regulated. Window opening was not allowed. It was
aimed to reach a median CO
level of 2000–
2500 ppm. Thus, conditions as can be found in
wintertime in poorly ventilated classrooms were
ÔBetterÕ: The mechanical ventilation was up-regu-
lated. It was aimed to reach a low median CO
of <1000 ppm. Window opening was not allowed.
Thus, conditions as recommended by expert panels
were simulated.
The reference period for the experimental condition
was the beginning of the first class hour until the end of
the d2-test in the fourth class hour (not counting
breaks) and thus resulted in a exposure duration of
typically 155 min. Experimental conditions were imple-
mented on 2 days per week. Thus, the data collection
period included 6 days in three consecutive weeks. For
each class, those weekdays were preferably selected as
test days, in which according to the timetable, all
students usually stayed in the classroom all morning (at
least from about 8 oÕclock until 11 oÕclock; i.e., German
class hours 1–4). Classes from the same school were
evaluated parallel during the same 3 weeks. Different
schools were assessed consecutively.
We randomized the unit Ôschool class,Õbecause air
quality could only be regulated classroom wise. In all
classrooms, the experimental condition in the first week
(the first two experimental days) was ÔusualÕ. The
sequence in the following 2 weeks – either worse/better
or better/worse – was randomized. If the air quality in
the classrooms within schools could be regulated
separately, classrooms were arranged in pairs and
randomization was performed within these pairs. In
that way, in each pair, one classroom was assigned the
sequence worse/better and the other the sequence
better/worse. Otherwise, the sequence of conditions
was determined for all classrooms within one school
combined. Allocation was performed by random
drawing of marked pieces of paper.
Concentration performance was assessed by the d2-test
(Bates and Lemay, 2004; Brickenkamp, 2002). The d2-
test is a one-page, paper-and-pencil test with 14 rows of
Effect of classroom air quality on studentsÕconcentration
the characters ÔdÕand ÔpÕ. The task is to mark as many
target characters as possible (a ÔdÕwith a total of two
dashes placed above and/or below) per row in a limited
time of 20 s. The test person is told verbally every 20 s
to move on to the next line, leaving the previous line
not fully examined. Based on the number of processed
characters and the number of errors, specific outcome
parameters can be determined. Those are Ôtotal number
of characters processedÕ(TN) as an indicator for
processing speed and the Ôtotal errorsÕ(TE, defined as
sum of incorrectly marked distractor characters plus
left-out targets) as an indicator of accuracy, as well as
ÔCPÕ(defined as the number of correctly marked target
characters minus incorrectly marked distractor char-
acters) as an indicator for overall concentration. In
RaBe, CP was used as a primary outcome. The d2-test
was applied at each of the six experimental days in the
fourth class hour. Trained study personnel instructed
the students on how to fill in the form according to
standard instruction. The students, teachers, and
instructors were blinded with respect to the experimen-
tal condition on the day of the test. Non-participating
children stayed in the classroom, and where given
different tasks during the time, participating children
filled out the questionnaire.
Other data collection
Sociodemographic data on the participating children
were collected via a standardized questionnaire for
parents. Characteristics of the schools and the class-
rooms were collected by a standardized documentation
sheet, which was filled out by study assistants.
Measurement of air quality
During the 3 weeks of data collection, we documented
the air quality in the respective classrooms. For this
purpose, we placed an air-quality sensor (Klimawa
ter MF420-IR-CTF; J. Dittrich Elektronic GmbH &
Co KG, Baden-Baden, Germany) in the middle of the
classroom above the heads of the children. For each
minute, the average room temperature (measurement
range, 0–50C), relative humidity (measurement range,
15–95%), and CO
level (measurement range, 0–
3000 ppm) were recorded. The median CO
temperature, and relative humidity in a classroom on
an experimental day were determined from the minute-
by-minute measurements during the first four school
hours (not counting the breaks) until the end of the d2-
test. In case of gaps in data of maximal length of ten
minutes (seven occasions), missing data were substi-
tuted by the average of the adjacent values. In case of
longer gaps (nine occasions), missing values were
substituted by measurement results from the second
day with the same experimental condition in the
respective classroom.
Statistical methods
We compared the distribution of baseline characteris-
tics of classes and students in the two experimental
arms (sequence usual–worse–better vs. sequence usual–
better–worse). Grade and sex of the students were
taken from school lists, other sociodemographic data
from the parental questionnaire. We determined rela-
tive poverty based on the equivalent household net
income, which was calculated from the household net
income and the number and age of the household
members weighted according to OECD (Bundesregie-
rung 2005). If the equivalent household net income
was <60% of the median equivalent net income in
Germany, relative poverty was categorized as ÔyesÕ.
Next, we described the CO
level in the classrooms
depending on the experimental condition as well as the
distribution of the parameters of the d2-test.
To determine the effect of the experimental condition
on the test results (CP, TN, and TE), we employed
three-level hierarchical linear models (Raudenbush and
Bryk, 2002). Hierarchical linear models are an exten-
sion of the linear model, which is used in RaBe to
account for the repeated measurements of students as
well as for the correlation between students belonging
to the same class. By defining test parameters on level
one, students on level two, and classes on level three,
the measured test parameters were examined within
students within classes. The model assumes normally
distributed errors on all three levels. Results of analysis
are given on an additive scale.
First, we set up to a growth model to model the
change in parameters with the repeated application of
the d2-test (i.e., the learning effect). We allowed a linear
and a quadratic term of growth. The inclusion of these
terms as fixed effects on level one as well as random
effects on level two and three was tested for significance
at P< 0.05 based on the likelihood ratio test. In
the resulting growth model, parameters to assess the
impact of air quality were included as follows:
We followed three different approaches to test our
hypothesis of air-quality effects on student perfor-
mance in the d2. First, we used experimental condition
(usual/worse/better) coded as dummy variables as
predictors of the CP and included the complete data
set. This analysis is in accordance with the design and
comparable to an intention-to-treat approach in clin-
ical studies. In an intention-to-treat approach, the
complete data set has to be included in analysis and
participants have to be analyzed as randomized irre-
spective of whether the treatment was in fact imple-
mented or not.
As in some occasions the study protocol was not
followed exactly, analysis was repeated using a reduced
data set after exclusion of observations with deviations.
Deviations from study protocol pertained to the
completion of the d2-test (30 s instead of 20 s given
Twardella et al.
for the completion of one of the 14 lines in the d2-test,
student did not use the prescribed sign to mark relevant
letters, student did not complete all 14 lines) as well as
to test conditions (the whole class left the classroom for
at least one hour during the morning, a part of the class
left the classroom for at least one hour during morning,
the class had physical education during the morning,
the regulation of the ventilation system did not work).
If the deviations from the study protocol were signif-
icantly associated with the test parameters in the
hierarchical model and thus could introduce confound-
ing, observations were excluded.
Finally, because of the variability of actual CO
levels within the study groups, in a third analysis, the
association of the actual CO
median with studentsÕ
concentration was estimated. In this analysis, instead
of the study condition, the median CO
levels were
introduced as a linear predictor in the model, as
ventilation rate was found to be linearly related to
student achievement (Haverinen-Shaughnessy et al.,
2011). Analysis was based on the reduced data set as
in the second analysis.
Below, we present results from three analyses:
analysis 1 with complete data and experimental con-
dition as predictor, analysis 2 with data cleaned for
study protocol deviations and experimental condition
as predictor, and analysis 3 with cleaned data and
median CO
level as a predictor. In sensitivity analyses
for the primary outcome CP, first analyses 1–3 were
repeated after exclusion of observations from the
ÔusualÕcondition. Secondly, the effect of air quality
on CP was determined in analyses 1–3 including only
those observations in condition ÔbetterÕ, in which the
level was <1000 ppm and those observations in
condition ÔworseÕin which CO
level was >2000 ppm.
All hierarchical models were implemented in the
software HLM 6.08.
About half of the participating students were of grade
3 and about half were girls (Table 1). In seven percent
of the students, parents reported dyslexia.
Classrooms were between 59 and 71 m
of size. The
mean number of students of about 24 and the mean
room volume of 215 m
resulted in a mean air volume
of 9.2 m
available for each student (range, 6.9–
14.9 m
). Median room temperature on a test day
ranged between 20.0 and 26.3C (median = 23.6C,
10% percentile 22.1C, 90% percentile 25.1C) and was
similar on days with ÔusualÕ,ÔworseÕ,orÔbetterÕcondi-
tion. Median relative humidity in a classroom on a test
day ranged between 15.0% and 42.5% (med-
ian = 31.5%, 10% percentile = 22.7%, 90% percen-
tile = 39.4%) and was higher on days in ÔworseÕ
(median = 35.0%) than in ÔbetterÕcondition (med-
ian = 26.9%).
Figure 1 shows an example of the progression of
level in one of the participating classrooms during
the six experimental days. The general pattern of CO
Table 1 Sociodemographic background of the children included in the RaBe study
Sequence of experimental condition
TotalUsual–better–worse Usual–worse–better
Grade 3 140 (55%) 64 (39%) <0.01 204 (49%)
Grade 4 114 (45%) 99 (61%) 213 (51%)
Girls 115 (45%) 86 (53%) 0.14 201 (48%)
Boys 139 (55%) 77 (47%) 216 (52%)
Place of birth (missing = 27)
Germany 224 (95%) 148 (96%) 0.58 372 (95%)
Other 12 (5%) 6 (4%) 18 (5%)
Dyslexia (missing = 29)
Yes 13 (6%) 13 (9%) 0.25 26 (7%)
No 222 (94%) 140 (92%) 362 (93%)
Hyperactivity (missing = 28)
Yes/do not know 27 (11%) 8 (5%) 0.03 35 (9%)
No 208 (89%) 146 (95%) 354 (91%)
Single parent (missing = 29)
Yes 32 (14%) 23 (15%) 0.70 55 (14%)
No 203 (86%) 130 (85%) 333 (86%)
Parental education (missing = 39)
Low 32 (14%) 21 (14%) 0.91 53 (14%)
Average/High 199 (86%) 126 (86%) 325 (86%)
Relative poverty
Yes 65 (26%) 33 (20%) 0.21 98 (24%)
No/unknown 189 (74%) 130 (80%) 319 (76%)
Missing refers to missing data because of incomplete fill-out of study questionnaires.
Subjects with missing data cannot be categorized.
Cochrane Mantel–Haenszel P-value of overall association to compare the distribution of
student characteristics in the two groups.
Fig. 1 Exemplary progression of CO
levels during the six
experimental days in one of the classrooms of the RaBe study
Effect of classroom air quality on studentsÕconcentration
level in schools, which is typical for classrooms relying
on natural ventilation, can be observed in our study
with classrooms relying on mechanical ventilation, too.
In the morning, CO
levels are low, increase during
school hours, decrease during breaks, and decrease
after students leave the school. In the experimental
condition, ÔusualÕ(blue lines) maximal CO
levels get
close to 1500 ppm, in the ÔbetterÕcondition (green lines)
levels were decreased, and in the ÔworseÕcondition
(red lines) increased. The gray shade marks the relevant
time period from the beginning of the first school hour
until the end of the d2-test. Looking at all classrooms,
the fluctuation of CO
levels in the ÔbetterÕcondition
was smaller (average standard deviation of the mean
levels = 141) than in the ÔworseÕcondition
(average standard deviation = 559). The distribution
of the minute-by-minute measurements in all class-
rooms is given in the Supporting information
(Table S1).
Table 2 shows the distribution of the median CO
values during this relevant time period for all 20
participating classes for the three experimental condi-
tions. It turned out to be difficult to regulate the
mechanical ventilation system in a way that ensured
the achievement of the planned CO
levels. Thus, only
on 20 of 40 days in condition ÔworseÕ, the median CO
level was between 2000 and 2500 ppm, and only on 22
of 40 days in condition ÔbetterÕ, the CO
level was
below 1000 ppm. However, on average, the median
level in the ÔworseÕcondition (2115 ppm) was
1070 ppm higher than the CO
level in the ÔbetterÕ
condition (1045 ppm), which is significant
(P< 0.0001) in the t-test.
In each data collection, round between 394 and 401
of the 417 students returned the d2-test, of which 391–
397 could be used to calculate the test parameters,
resulting in overall 2366 values for each d2-parameter.
At the first occasion (round A), the mean values were
101 for CP, 270 for TN, and 13.7 for TE. During the
following occasions, the mean values of CP and TN,
but not TE, increased (Figure 2). Overall, values
ranged from )75 to 298 for CP, 102–657 for TN, and
0–324 for TE.
In the hierarchical linear model, we could not
observe a significant effect of the experimental condi-
tion on the primary parameter CP (Table 3). The CP
was decreased by 1.11 points at ÔworseÕin comparison
with ÔbetterÕair quality in analysis 1; however, this
difference was not statistically significant (95%
Fig. 2 Distribution of the test values during the six experimental
days (round A to F) by sequence of experimental conditions.
The boxplots show the median, the 25th and 75th percentile
(boundary of the box), the 10th and 90th percentile (whiskers)
and the 5th and 95th percentile (dots)
Table 2 Distribution of the median CO
levels in the 20 classrooms of the RaBe study by
experimental condition
Usual Worse Better
N(classrooms) 40 40 40
Mean 1371 2115 1045
<1000 ppm 5 1 22
1000–<1500 ppm 23 1 17
1500–<2000 ppm 7 14
2000–2500 ppm 5 18 1
>2500 ppm 6
Twardella et al.
confidence interval CI )2.44 to 0.22). No significant
effect could be found, if observations were excluded
(analysis 2) or if the actual median CO
level instead of
the experimental condition was tested (analysis 3). In
sensitivity analysis after exclusion of observations from
round A and B, effect estimators were even lower and
also not statistical significant (analysis 1: 0.45, 95% CI
)1.96 to 1.07; analysis 2: 0.35, 95% CI )1.76 to 1.06;
analysis 3: 0.37, 95% CI )1.56 to 0.82). If only
observations in condition ÔbetterÕwere included, in
which the CO
level was <1000 ppm and only those
observations in condition ÔworseÕin which CO
was >2000 ppm, again, no significant effect of exper-
imental condition on CP was found (data not shown).
Similarly, no significant effect of experimental con-
dition or median CO
level on TN could be observed
(Table 3). With respect to TE, though, a significant
result was produced. In analysis 1, using all observa-
tions, the number of errors was increased by 1.34 (95%
CI )0.03 to 2.70) in the worse air-quality condition
compared to better air quality. If observations with
deviation from the protocol were excluded, the effect
became stronger and statistical significance was
reached. In analysis 2, the number of errors was
significantly increased by 1.65 (95% CI 0.42–2.87) in
case of worse air quality. Also, the TE was increased
with increased median CO
level. With an increase in
median CO
by 1000 ppm, TE increased by 1.19 (95%
CI 0.30–2.07).
We could recruit 20 school classes in the RaBe study,
of which 417 students participated in data collection.
The regulation of the mechanical ventilation system to
reach the planned CO
levels in the classrooms turned
out to be challenging. However, a significant gradient
of 1070 ppm CO
between ÔworseÕand ÔbetterÕexper-
imental conditions could be achieved, indicating a
potentially relevant difference in air quality. The
hypothesis that worse air quality as indicated by
increased CO
level causes a reduction in CP in
students could not be confirmed with our study. While
the estimators for the effect air quality on CP did not
reach statistical significance, all point in the same
direction, that is, low performance at worse air quality.
Furthermore, results indicate a negative effect of worse
air quality on accuracy.
In the past, few studies have been conducted on the
CP of students and their relation to air quality as
indicated by CO
levels. In a review from the year 2005,
only one study was included which evaluated studentsÕ
performance depending on air quality (Mendell and
Heath, 2005). In this experimental study, a negative
effect of high CO
level on reaction time and perfor-
mance could be observed. Since then, seven other
studies on this issue have been published.
In an observational study from the USA, the air
quality with closed windows and active ventilation
system was measured in classroom of classes grade 5
during one school day and the ventilation rate
deduced. Standardized test scores based on math and
reading skills were obtained for the students. In the
pilot analysis of 50 school classes, the association
between the ventilation rate and test results was
significant only at P< 0.1 but not at P< 0.05
(Shaughnessy et al., 2006). In the main study, using
only data of those classes, in which ventilation rates
were below recommended guidelines (N= 87), a
significant association between the ventilation rate
and the results of the math and reading tests could
be observed (Haverinen-Shaughnessy et al., 2011). This
study provides indication of the relevance of indoor air
quality for long-term student achievement as indicated
by standardized tests. However, because of the obser-
vational design, the validity is limited.
The five remaining studies are experiments, in which
students were tested at different ventilation rates
resulting in different CO
levels. Naturally, only
short-term performance at the time of the specific air
quality can be measured in such a design. In Denmark,
two experimental studies have been undertaken in two
classrooms of one school with students of age ten to
twelve (Wargocki and Wyon, 2007a,b). Both were
cross-over trials in which CO
levels were regulated by
the ventilation system. However, additional window
opening was allowed. The performance of the students
was assessed by seven exercises exemplifying different
aspects of schoolwork. In the first experimental study,
the difference in CO
levels between study conditions
was low (on average 1270 with low and 920 ppm with
high ventilation), but still a significant higher speed of
work with high ventilation was found for five of the
Table 3 Estimators of the effect of the experimental condition (analyses 1 and 2) and the
actual CO
level (analysis 3) on the d2-test parameters with 95% confidence intervals
Number of observations in analysis
Reduction in test parameter (95% CI)
Analysis 1 Analysis 2 Analysis 3
Concentration performance
Worse compared to
better air quality
N= 2366
)1.11 ()2.44; 0.22)
N= 1962
)0.55 ()1.83; 0.73)
Median CO
1000 ppm)
N= 1962
)0.76 ()1.86; 0.34)
Total number of characters
processed (TN)
Worse compared to
better air quality
N= 2366
)0.88 ()3.84; 2.08)
N= 2038
)0.11 ()3.23; 3.01)
Median CO
1000 ppm)
N= 2038
)0.88 ()3.46; 1.70)
Total errors (TE)
Worse compared to
better air quality
N= 2366
1.34 ()0.03; 2.70)
N= 2254
1.65 (0.42; 2.87)
Median CO
1000 ppm)
N= 2254
1.19 (0.30; 2.07)
Effect of classroom air quality on studentsÕconcentration
seven exercises (Wargocki and Wyon, 2007b). No effect
on accuracy was found. In the second study, because of
missing data, only four of the seven exercises could be
analyzed (Wargocki and Wyon, 2007a). For none of
those exercises, a significant influence of ventilation
was observed. The difference in CO
level in this trial
was even lower (about 775 vs. 1000 ppm).
In a study from England, computer-based tests were
conducted on 10 days in one class with students aged
ten to eleven years (Coley et al., 2007). Poor air quality
with mean CO
levels of 2909 ppm was reached by
restriction of window opening on 5 days, while on the
other 5 days by opening of the windows mean CO
levels of 690 ppm were achieved. For three of the 13
parameters of cognitive function, a significant influence
of air quality was observed. At good air quality,
reaction time was reduced and attention increased,
while at poor air-quality calmness was increased. It
remains unclear from the publication, whether the
learning effect was considered in the analysis. Further-
more, because study conditions were established by
window opening, students and teachers were not
blinded with respect to the study condition, which
could have an impact on test results.
In a second publication from the UK, preliminary
results of an experimental cross-over trial in one school
were reported (Bako-Biro et al., 2007). A direct air
supply system through the window was used to
establish two study conditions: (i) provide outdoor
air (mean CO
593–783 ppm); (ii) recirculate the
classroom air (mean CO
1638–4093 ppm). StudentsÕ
performance was tested with a 40-min paper-based test
(reading comprehension, addition, subtraction). No
effect of study condition on reading comprehension or
subtraction, but a significant improvement of addition
with provision of fresh air was observed.
Lastly, an experimental cross-over study in six
classes in Switzerland was conducted, and performance
assessed with the d2-test in students aged 15–16 years
(Ribic, 2008). Air quality was influenced by regulations
on window opening and CO
levels of 600–800 ppm for
good air quality and of at least 3000 ppm for low air
quality established. A significantly reduced CP was
observed at low air quality. However, students and
teachers were not blinded with respect to the study
condition. Furthermore, it remains unclear whether the
tests were completed under the same conditions, for
example, at the same time of day.
The above-mentioned studies mostly reported a
significant effect of air quality on at least some
parameters of studentsÕperformance. However, in
some studies, methodological limitations confine the
interpretation. The lack of blinding may have caused
false-positive associations. Parallel testing of multiple
performance parameters without correction of the P-
value weakens the interpretation of statistical signifi-
cance. Furthermore, the correlations between students
coming from the same class have not been accounted
for in the statistical analysis of most of the mentioned
studies. To neglect correlation between observations –
a clustered design – leads to an underestimation of
variance and thus can cause false statistical significance
(Donner and Klar, 2000). In our study, we tried to
avoid these methodological problems. We included a
relatively large number of classes and students and
were able to account for correlations between classes in
statistical analysis. Students, teachers, and test instruc-
tors were blinded with respect to the experimental
condition. We defined CP as our primary outcome to
avoid multiple testing. Results of secondary outcome
analysis are interpreted as explorative results.
With our RaBe study, we were not able to confirm
the results of former studies, which found a decreased
CP of children exposed to low air quality as indicated
by high CO
levels. The following limitations have to
be accounted for when interpreting our study results.
Mechanical ventilation in classrooms is very seldom in
Germany, and only recently, if schools are newly built
or if major renovations are undertaken, ventilation
systems are installed. Thus, although we aimed for 24
classes with 600 students, only 20 classes with 417
students took part. With a larger sample size, the effect
estimator might have become statistically significant.
However, even if statistically significant, it might be
questionable, whether a diminishment by 1% (effect
estimator 1.11 points, mean value in round A 101
points) would be regarded as a relevant change.
It was not always possible to adapt the timetable and
routine in schools to the optimal study design. Thus,
we could not avoid that in some cases classes had
physical education or left the classroom in the morning
before the test. We tried to compensate for these
deviations from study protocol by running analysis 2.
However, results did not change substantially. Still,
school is not a laboratory, and it is difficult to
standardize all possible influential factors.
Confounding is an unlikely explanation for the
observed results. First of all, one has to consider that
RaBe is a cross-over study, and thus, each student
serves as its own control. Thus, in the statistical model,
confounding of the effects size by any characteristic of
the student is not possible. However, a statistical
interaction cannot be excluded, which would be
interpreted as a differential effect of air quality
depending on student characteristics. As the model
that was used for RaBe was based on the assumption
that the effect of air quality on CP does not depend on
the characteristic of the students, it does not account
for interactions.
Secondly, RaBe was a randomized study. Random-
ization per se is a method to control confounding by
design (Rothman et al., 2008). If randomization is
successful, confounding by know as well as unknown
factors is prevented and thus adjustment in statistical
Twardella et al.
analysis not needed. However, the number of random-
ized units (classes) is relatively low and thus some
residual confounding possible.
To tackle the issue of residual confounding, the
following steps were taken: First of all, because of the
obvious strong enhancement of test parameters with
repeated testing, we assessed the effect of experimental
condition within a growth model, which captured this
change over time. Thus, all reported results are adjusted
by the Ôlearning effectÕ. Secondly, for the primary
outcome CP, we conducted adjusted analysis. Sociode-
mographic characteristics as well as room temperature
and relative humidity were assessed for relevance in the
growth model, and the effect of experimental condition
on CP was tested in an model adjusted for those factors
showing P< 0.1 in the growth model. In this model,
only the factor Ôsingle parentÕshowed a significant
interaction. While in the adjusted model there was a
significant reduction of CP in Ôlower air qualityÕ()1.85,
95% CI )3.38 to )0.33), in children with a single parent
this was changed to an increase by 2.83. There is no
obvious explanation for this interaction and chance
result because multiple testing is possible.
The CO
measurement range had an upper detection
limit of 3000 ppm, which was reached on eight of the
120 test occasions. The exceedance lasted for 1, 2, 3, 6,
7, 18, 39, or 49 min, respectively, on these eight
occasions. In none of the classes, the percentage of
the minute-by-minute CO
values on one test day,
which were above the detection limit reached 50%.
Thus, the calculation of median CO
levels was not
affected by this measurement limitation.
We were not able to achieve the exact CO
levels as
planned by regulation of the mechanical ventilation
system. However, first, we could show that on average
levels were considerably higher in the ÔworseÕ
condition than in the ÔbetterÕcondition. Secondly, in
sensitivity analysis, we run a model in which only those
observations were included, in which the specifications
were achieved. Still in this analysis, no effect of
experimental condition on CP could be observed.
Thus, it seems implausible that the difficulties in
achieving specific CO
levels were responsible for the
absence of the effect.
Although students, teachers, and test instructors
were blinded with respect to the experimental condi-
tion, we cannot exclude that at least some subjects
could have become aware of the experimental condi-
tion during the test period because of body odors and
general stuffiness in the room. Thus, the reported
association might at least partly be due to insufficient
The RaBe study differs with respect to the range of
observed CO
levels from other studies. Effects of CO
levels might only become evident when comparing
more distinct groups or air quality or achieving CO
levels far below 1000 ppm.
In RaBe, student performance was characterized
with the d2-test. In contrast to usual school examin-
ations, the test does not require specific skills but aims
to assess the aspect of concentration as a basic
prerequisite for the provision of any achievement
(Brickenkamp, 2002). In several studies, a positive
correlation between the CP derived from the d2-test
and intelligence and achievement motivation has been
observed (see for example Romainczyk, 2008 or
Schaal, 2004). It has also been shown that parameters
of the d2-test correlate with school marks (Brickenk-
amp, 2010). In some of the other published studies,
school work was used to characterize studentÕs perfor-
mance. While performance at school work is a more
direct measurement of a relevant outcome, which can
be obtained in a usual school situation, the knowledge
and skills of the students will impact the performance
to a larger extent particularly if the same examination
will be repeated at different times. Both studies using
tightly controlled tests as in our study (Coley et al.,
2007; Ribic, 2008) as well as studies using school
examinations to characterize student performance
(Wargocki and Wyon, 2007a) obtained a significance
influence of air quality. There are no indications for a
differential effect. The d2-test does assess concentration
only. Other possible effects of air quality such as an
increase in health symptoms and resulting absenteeism
are not covered.
In our study, air quality is described by carbon
dioxide concentration and not by ventilation rates. By
design, carbon dioxide levels were used to define
experimental groups. Carbon dioxide is a well-accepted
indicator of air quality, and parameters of ventilation
engineering are of less importance in a country such as
Germany with the majority of schools relying on
natural ventilation. Furthermore, as in RaBe class-
rooms serve as their own control and CO
can be assumed constant within classrooms, differences
in ventilation rates in a classroom at different exper-
imental conditions will highly correlate with differences
in CO
levels. Even if ventilation rates were derived,
results of analyses 1 and 2 would not change because
the predictor variable was experimental group.
Using the median CO
levels, the situation in the
classrooms in RaBe can be easily compared to real-life
situation that is observed in classrooms in Germany as
has been described in the past (Fromme et al., 2008).
is a well-known indicator of air quality and has
been related to health effects in the past (see for
example Shendell et al., 2004; Seppa
¨nen et al., 1999).
The reason for the absence of a significant effect on
the CP in our study does not become clear. We can
speculate, but not prove with data, that motivation of
students might have played a role. In RaBe, we
observed that the children looked forward to the test
and were highly motivated. The test was an interrup-
tion from the usual school work, and the children may
Effect of classroom air quality on studentsÕconcentration
have perceived it as a welcome diversion. In addition,
the d2-test lasts only about five minutes and thus
requires only short-term attention. It could be that for
a short period of time children can activate resources
even if exposed to low air quality. This would explain
why we could not observe an effect on the CP. It
cannot be excluded that in longer tests low air quality
eventually diminishes attention and CP.
Furthermore, while we could not observe an impact
on the primary outcome CP, our data do suggest a
decrease in the secondary outcome accuracy if children
are exposed to poor air quality. In comparison with the
mean total error of between 7.8 and 13.7, an increase in
1 to 1.5 points is an increase by about 10% and thus of
relevance. The association is significant if looking at
the effect of the experimental condition as well as
looking at the effect of the CO
level itself. In line with
the previous interpretation, one could argue that
children are able to achieve a normal CP for a short
time even if exposed to poor air quality, but while they
are able to sustain the processing speed they risk more
errors. Thus, tasks that require high precision might be
more strongly affected by poor air quality. However,
this result is in contradiction with the study of
Wargocki et al. in which increased ventilation in-
creased the speed of work but did not have any effect
on accuracy (Wargocki and Wyon, 2007b). The reason
for this contradictory result remains unclear.
We were not able to show a negative effect of air quality,
which was similar to that observed at wintertime in
classrooms relying on natural ventilation, on the CP of
students. Secondary analysis suggests an effect of air
quality on the processing accuracy. Our results are in
conflict with earlier studies, which suggested the pres-
ence of an effect on concentration. The causes of the
conflicting results remain unclear. It cannot be excluded
that significant results of earlier studies are at least partly
due to methodological limitations. While we tried to
avoid these limitations in RaBe, bias as a result of
varying circumstances in schools that are difficult to
standardize cannot be ruled out. One possible explana-
tion could be that poor air quality does not impair short-
term CP (as measured by the d2-test in our study) but
only shows its effect in longer concentration efforts.
Furthermore, air quality might be more relevant for the
precision than for the processing speed. Further research
is needed to clarify the relevance of indoor air quality on
long-term CP of students.
We wish to thank the participating schools, the
teachers, and students for their support of the study
as well as Ramona Grahle, Annemarie Hiergeist, Vera
Hoffmann, Petra Kaplan, Petra Panenka, Annette
Mangstl, Evelyn Schmidt, Ute Warmuth, and Simone
Zyzik-Zinn for their engagement in the data collection
and data management and Angelika Schwaiger for
assistance in organizational matters.
Competing interests
The study was funded by the Deutsche Bundesstiftung
Umwelt (DBU), Az. 27549 – 25. The DBU was not
involved in the study design; in the collection, analysis,
and interpretation of the data; in the writing of the
report; or in the decision to submit the paper for
publication. The manuscript reflects the perception and
opinion of the contractor. It does not represent the
opinion of the DBU.
Supporting Information
Additional Supporting Information may be found in
the online version of the article:
Table S1 Distribution of the minute-by-minute mea-
surements of carbon dioxide concentration in each
classroom during the six 4-hour test periods.
Please note: Wiley-Blackwell are not responsible for
the content or functionality of any supporting materi-
als supplied by the authors. Any queries (other than
missing material) should be directed to the correspond-
ing author for the article.
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Effect of classroom air quality on studentsÕconcentration
... Finally, 30 studies met the eligibility criteria and were included in the review. Additionally, 9 papers were identified via the snowballing process, totalizing 39 studies: 10 targeting Air Pollution [41][42][43][44][45][46][47][48][49][50], 17 on Green Spaces [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67], and 12 on Active Travel to school [68][69][70][71][72][73][74][75][76][77][78][79]. No eligible intervention to reduce road or air traffic noise at school was found. ...
... In total, 16 of the 39 studies were implemented in Europe, including Denmark [50,79], Germany [49], Italy [61], The Netherlands [47,48,65,66], Portugal [67], Spain [42], Sweden [44,55], and the United Kingdom [45,60,62,63]. Most of the active travel interventions were conducted outside Europe, including the United States [68][69][70][71]74], Canada [75,77,78] and New Zeeland [72,73,76] Two interventions were held in Seoul, Korea [41,59], and one in Victoria, Australia [51]. ...
... The level of intervention implementation varied substantially across studies, from classroom level for all air pollution interventions [41][42][43][44][45][46][47][48][49][50]; at classroom [54][55][56][57][58][59][60]66,67] or school levels [51][52][53][61][62][63][64][65] for green spaces interventions. Most active travel studies involved national-wide programs evaluated at school [76,79], district [69], region [72,73], province [75], and state or multistate [68,70,71,77,78] levels. ...
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Environmental exposures are associated with children’s health. Schools are often urban exposure ‘hotspots’ for pollution, noise, lack of green space and un-walkable built environments. The aim of this systematic review was to explore the impact of school-based interventions on the modification of indoor and outdoor stressors related to the built and natural environment on children’s exposure and health. A systematic review of seven databases was performed. We included quantitative studies on children aged 5–12, which reported intervention delivered within school settings aimed at addressing key environmental exposures including air pollution, green spaces, traffic noise or active travel; and reported physical and mental health, physical activity or active travel behavior. The quality of studies was assessed and interventions were described using a standardized framework. A narrative synthesis approach was used to describe the findings. Thirty-nine papers were included on three main intervention types: improve indoor air quality by the increase of ventilation rates in classrooms; increase children’s green time or greening schools, and multicomponent interventions to increase active travel to school by changes in pedestrian facilities. No eligible intervention to reduce traffic noise at school was found. Increasing ventilation rates improved short-term indoor air quality in classrooms, but the effect on cognitive performance was inconsistent. Greening schools and increasing children’s green time have consistent positive effects on cognition and physical activity, but not in behavior. Multi-component interventions can increase walking and cycling after three years. Overall, the studies were rated as having poor quality owing to weak study designs. We found modest evidence that school-based built and natural environment interventions can improve children’s exposure and health.
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... Bloch-Salisbury et al. [15] reported that neither the partial pressure of carbon dioxide in the arterial blood (PaCO 2 ) increases (mean = 47 mmHg) nor decreases (mean = 38 mmHg) from the resting level (mean = 30 mmHg) would affect the cognitive performance, which was probably attributed to the low statistical power of small sample size. In contrast to the addition of pure CO 2 , an impaired cognitive performance was more commonly observed under restricted ventilation conditions [16][17][18][19]21]. Only one study of astronaut-like subjects indicated no significant changes in SMS and cognition test batteries as concentration rose from 600 ppm to 5000 ppm via decreased ventilation rates [20]. ...
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Aminated adsorbents are efficient for CO2 capture from indoor air, but mild-temperature regeneration is challenging. In this study, the epoxide-modified pentaethylenehexamine (PEHA) was loaded in the modified activated carbon fiber felt (ACF), and the formed hydroxyl groups after the reaction of 1,2-epoxybutane (EB) with PEHA made the adsorbent stable and efficient for CO2 adsorption and desorption. The optimized adsorbent, denoted as EB-PEHA-ACF, had the CO2 adsorbed amount of 60 mg/g in the simulated indoor air with 1000 ppm CO2 and 50% relative humidity. The adsorbent exhibited fast desorption kinetics, and nearly 100% of adsorbed CO2 could be desorbed within 25 min even at 60 ℃. Especially, this spent EB-PEHA-ACF could be successfully regenerated at 50 ℃, and the adsorbent showed almost stable adsorption capacity in ten adsorption and regeneration cycles. Fourier Transform Infrared (FTIR) analysis showed that the functional group peaks of EB-PEHA-ACF were unchanged before and after the ten CO2 adsorption-desorption cycles.
Post-occupancy evaluation (POE), a comprehensive evaluating approach for building operational performance, has attracted considerable research attention worldwide. This research is the first inclusive scientometric review of POE research, aiming to systematically analyse the and visualise the existing POE literature. Through four steps of data collection, 1,351 POE articles over past two decades from Web of Science have been gathered and analysed by using CiteSpace. This research retrospectively traces the development of POE research from 2002 to 2021 The review identifies that the main focuses of POE research have dynamically formed with the interdisciplinary characteristic. By generating scientific knowledge maps of the co-occurrence, emergence and clusters of keywords, it intuitively shows that some particular keywords, such as ‘performance’, ‘thermal comfort’ and ‘IEQ’, are much more obvious than others; a series of hotspots have emerged in different times; and three main themes are outlined and critical reviewed based on scientometric findings, i.e., energy consumption monitoring of building operations, indoor environmental quality and the evaluation of occupants’ perceptions. This research has implications for policy makers, developers and researchers in the building sector by providing them with a detailed account of status quo and the trend of POE.
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Η μελέτη αυτή παρουσιάζει μια εξαμηνιαία έρευνα (από Ιανουάριο έως Ιούνιο του 2019) αναφορικά με τα επίπεδα συγκέντρωσης του διοξειδίου του άνθρακα (CO2) σε τάξεις ενός δημοτικού σχολείου. Το CO2 είναι ένα βασικό ανθρωπογενές αέριο ρύπανσης, που συνδέεται στενά με την ποιότητα του εσωτερικού αέρα (Indoor Air Quality/IAQ), που, με τη σειρά του, είναι ένας σημαντικός παράγοντας της εσωτερικής περιβαλλοντικής ποιότητας (Indoor Environ mental Quality/IEQ). Τα υψηλά επίπεδα CO2 έχουν αποδειχθεί ότι επηρεάζουν σημαντικά τη συνολική ποιότητα του εσωτερικού αέρα (IAQ), επηρεάζοντας την υγεία και τη γνωστική λειτουργία, οδηγώντας σε πολλές απουσίες από τα σχολεία (ή την εργασία), σε προβλήματα υγείας και σε μειωμένα ακαδημαϊκά (ή παραγωγικά) αποτελέσματα. Δεδομένου ότι τα ελληνικά σχολεία, στην πλειονότητά τους, δεν έχουν τεχνητά συστήματα μόνιμου εξαερισμού στα κτίριά τους, όπως και το σχολείο της μελέτης, η έρευνα προσπάθησε να διαπιστώσει εάν ο φυσικός αερισμός (παράθυρα) και ο βοηθητικός αερισμός (ανεμιστήρες) είναι αρκετά για να διατηρήσουν τα επίπεδα του CO2 σε αποδεκτά επίπεδα, όπως καθορίζουν τα πρότυπα ασφαλείας (<1000 ppm). Ένας αισθητήρας (Kane Alert CO2) τοποθετούνταν εναλλακτικά σε δυο αίθουσες διδασκαλίας και κάθε 10 λεπτά καταγραφόταν το επίπεδο του CO2 σε ppm (καθώς και η θερμοκρασία δωματίου σε C) κατά τη διάρκεια των μαθημάτων της τάξης. Ο βαθμός συσσώρευσης του CO2 στην τάξη αξιολογήθηκε σε σχέση με τη θερμοκρασία δωματίου, το μέγεθος της τάξης, το ύψος της τάξης, τον αριθμό των μαθητών, τον αριθμό των ανοιχτών παραθύρων, το μέγεθος των παραθύρων, το αν η πόρτα της αίθουσας ήταν ανοικτή ή όχι, το μέγεθος της πόρτας, και συνδυάστηκε με εξωτερικά μετεωρολογικά δεδομένα (θερμοκρασία, ταχύτητα ανέμου, βροχή, ατμοσφαιρική πίεση) που ανακτήθηκαν από το Εθνικό Αστεροσκοπείο Αθηνών (Μετεωρολογικός Σταθμό Περιστερίου, που απέχει 1,8 χλμ. από το σχολείο) . Το επίπεδο του CO2 που συσσωρεύεται στις σχολικές τάξεις κατά τη διάρκεια των μαθημάτων αποτελεί ένα ισχυρό δείκτη για την επίτευξη καλής σχολικής, γνωστικής και μαθησιακής, επίδοσης και η γνώση του γεγονότος αυτού μπορεί να οδηγήσει σε αλλαγές συμπεριφοράς (συνεχής συνειδητοποίηση των επιπέδων IAQ, άνοιγμα παραθύρων κ.λπ.) που θα εξασφαλίζουν τα αποδεκτά επίπεδα CO2 μέσα στη σχολική αίθουσα και, συνεπακόλουθα, θα εξασφαλίζουν καλύτερα ακαδημαϊκά αποτελέσματα για τους μαθητές των σχολείων μας. This is a presentation of a six- month period survey (from January to June 2019) on the Carbon Dioxide (CO2) levels in 2 primary school’s classrooms. CO2 is a basic anthropogenic pollutant gas, closely associated with Indoor Air Quality (IAQ) and a major factor in Indoor Environmental Quality (IEQ). Higher levels of CO2 have been proved to lower significantly the overall IAQ, affecting health and cognitive function, leading to many absences from schools (or work), to health problems and deteriorating academic (or work production) results. Given the fact that Greek schools, in their vast majority, lack any artificial permanent ventilation systems in their buildings, such as the school in consideration, the survey tried to establish whether natural ventilation (windows) and assisted ventilation (fans) was enough to keep the CO2 levels within accepted standards (<1000 ppm). A sensor was placed in two classrooms (Kane Alert CO2) and the CO2 level (in ppm) plus room temperature (in C) was recorded every 10 mins during class lessons. The rate of CO2 accumulation in the classroom was evaluated in reference with room temperature, classroom size, classroom elevation, students’ number, number of open windows, size of windows, whether door was open or not, size of door, whether radiators (cold period) or fans (warm period) were working and general official meteorological data (temperature, wind speed, air pressure, rain) retrieved from National Observatory of Athens (Peristeri meteorological station, which is situated just 1,8 km away from the case school). Knowing the CO2 levels in school classrooms during lessons is a strong indicator of the resultant school cognitive and learning productivity and can lead to behavioural changes (constant awareness for “feeling” IAQ levels, opening of windows, etc.) that can keep CO2 levels within accepted standards and thus secure better academic results for schools and their students.
This study aims to investigate if a relationship can be established between measured indoor conditions and student performance in classroom settings. Ten classrooms in five Victorian schools in Australia were selected to monitor indoor conditions and measure student attention and concentration performance using a neuropsychological assessment, d2 Test of Attention. Correlation analysis revealed that the student performance parameters, particularly TN (reaction time, speed) and CP (accuracy), established a low to moderate correlation with most of the indoor condition parameters except CO2 concentration level. An exploratory stepwise multiple regression analysis identified that the common predictors of TN are relative humidity (RH), mean radiant temperature (MRT) and PM2.5 and the common predictors of CP are MRT and PM2.5. Interestingly, relative humidity (RH) and CO2 concentration level are the important predictors of both TN and CP among the seven environmental variables in the hierarchical multiple regression model when controlling the non-environmental variables such as student age and school terms. As thermal comfort related variables such as air temperature, air velocity and MRT were correlated with school terms due to seasonal changes, they contributed to the shared variance along with school terms in the regression model. Understanding the unique and shared contribution of the indoor condition parameters to student performance can help to develop strategies to improve school building performance.
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Research shows that poor indoor air quality (IAQ) in school buildings can cause a reduction in the students' performance assessed by short-term computer-based tests; whereas good air quality in classrooms can enhance children's concentration and also teachers' productivity. Investigation of air quality in classrooms helps us to characterise pollutant levels and implement corrective measures. Outdoor pollution, ventilation equipment, furnishings, and human activities affect IAQ. In school classrooms, the occupancy density is high (1.8-2.4 m 2 /person) compared to offices (10 m 2 /person). Ventilation systems expend energy and there is a trend to save energy by reducing ventilation rates. We need to establish the minimum acceptable level of fresh air required for the health of the occupants. This paper describes a project, which will aim to investigate the effect of IAQ and ventilation rates on pupils' performance and health using psychological tests. The aim is to recommend suitable ventilation rates for classrooms and examine the suitability of the air quality guidelines for classrooms. The air quality, ventilation rates and pupils' performance in classrooms will be evaluated in parallel measurements. In addition, Visual Analogue Scales will be used to assess subjective perception of the classroom environment and SBS symptoms. Pupil performance will be measured with Computerised Assessment Tests (CAT), and Pen and Paper Performance Tasks while physical parameters of the classroom environment will be recorded using an advanced data logging system. A total number of 20 primary schools in the Reading area are expected to participate in the present investigation, and the pupils participating in this study will be within the age group of 9-11 years. On completion of the project, based on the overall data recommendations for suitable ventilation rates for schools will be formulated. r A review of over 300 peer-reviewed articles of indoor air quality (IAQ), ventilation and building-related health problems in schools [1] has shown that ventilation is inadequate in many classrooms and was considered to be the main cause of health symptoms. Mendell and Heath [2] review evidence that certain conditions commonly found in US schools have adverse effects on the health and the academic performance of many of the more than 50 million US school children. They propose actions throughout the life of each existing and future school building to include adequate outdoor ventilation, control of moisture, and avoidance of indoor exposures to microbiologic and chemical substances considered likely to have adverse effects. A recent Dutch study [3] carried out in homes and in classrooms also showed that pupils' health appear to be associated with both the school and domestic exposure. Poor IAQ in schools was indicated; out of the 11 classrooms studied CO 2 levels were all above the recommended level of 1000 ppm with only one exception. Scandinavian research has shown that poor IAQ in school buildings can cause a reduction in the students' performance, whereas good air quality in classrooms can enhance children's concentration and also teachers' productivity [4,5]. An International Society for Indoor Air Quality (ISIAQ) Task Force report on Nordic schools [6], has identified the following areas for further research: ARTICLE IN PRESS 0360-1323/$-see front matter r
Conference Paper
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Associations between classroom ventilation and pupils' performance were investigated in primary schools in the United Kingdom. The concentration of carbon dioxide and other parameters were monitored for three weeks in two selected classrooms in each school. A direct air supply system through the windows was used to alter the ventilation rates in the classrooms. The system was set either to provide outdoor air or to re-circulate the classroom air while all other physical parameters were left unchanged. Computerised Assessment Tests and Paper-based Tasks were used to evaluate pupils' performance. Pupils' perceptions about the classroom environment, comfort, general mood and hunger were assessed on subjective scales. The present paper shows preliminary results obtained for one primary school out of eight being studied. Due to the intervention the fresh air supply increased from 0.3-05 to 13- 16 L/s per person that increased pupils' work rate by ~7% in addition (p
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Two independent field intervention experiments were carried out in school classrooms in late summer (in 2004 and 2005). The air temperature was manipulated by either operating or idling split cooling units installed for the purpose. In one of these experiments, the outdoor air supply rate was also manipulated. The conditions were established for one week at a time in a blind crossover design with repeated measures on two classes of 10- to 12-year-old children. Six to eight exercises exemplifying different aspects of schoolwork (numerical and language-based) were performed as part of normal lessons. Pupils indicated their environmental perceptions and the intensity of any symptoms on visual analogue scales. Their thermal sensation changed from slightly too warm to neutral, and the performance of two numerical and two language-based tests was significantly improved when the temperature was reduced from 25°C to 20°C (77°F to 68°F). When the outdoor air supply rate was increased from 5.2 to 9.6 L/s (11.0 to 20.3 cfm) per person, their performance of four numerical exercises improved significantly, confirming the results of previously reported experiments in the same series. The above improvements were mainly in terms of the speed at which tasks were performed, with negligible effects on error rate. Most school classrooms worldwide experience raised air temperatures during increased thermal loads, e.g., in warm weather; these results show that providing some means of avoiding elevated temperatures would improve educational attainment.
Several studies have suggested that recommended ventilation rates are not being met within schools. However these studies have not included an evaluation of whether or not this failure might have an impact on pupil performance and learning outcome. The work reported here was designed as an initial investigation into this question. Using the Cognitive Drug Research computerised assessment battery to measure cognitive function, this study demonstrates that the attentional processes of school children are significantly slower when the level of CO2 in classrooms is high. The effects are best characterised by the Power of Attention factor which represents the intensity of concentration at a particular moment, with faster responses reflecting higher levels of focussed attention. Increased levels of CO2 (from a mean of 690 ppm to a mean of 2909 ppm) led to a decrement in Power of Attention of approximately 5%. Thus, in a classroom where CO2 levels are high, students are likely to be less attentive and to concentrate less well on what the teacher is saying, which over time may possibly lead to detrimental effects on learning and educational attainment. The size of this decrement is of a similar magnitude to that observed over the course of a morning when students skip breakfast.
Two independent field intervention experiments were carried out in mechanically ventilated classrooms receiving 100% outdoor air. Outdoor air supply rate and filter condition were manipulated to modify indoor air quality, and the performance of schoolwork was measured.The conditions were established for one week at a time in a blind crossover design with repeated measures on 10- to 12-year-old children in two classes. Seven exercises exemplifying different aspects of schoolwork (numerical or language-based) were performed as part of normal lessons by pupils who also marked visual analogue scales to indicate their environmental perceptions and the intensity of any symptoms. The children indicated that the air was fresher but otherwise perceived little difference when the outdoor air supply rate increased from 3.0 to 8.5 L/s (6.4–18 cfm) per person, while the speed at which they performed two numerical and two language-based tasks improved significantly. A significant effect of ventilation rate was observed in 70% of all the statistical tests for an effect on work rate, but there were no significant effects on errors. The effects were probably due to improved air quality in the classrooms as judged by a sensory panel of adults blind to conditions, as perceived by children, and as indicated by the reduction in the average CO2 concentration from 1300 to 900 ppm, taking this as a marker of reduced bioeffluent concentration. It was not possible to test the effect of replacing a soiled filter with a new one because very little dust had been retained by the “used” filter and because of an incompletely balanced design. The unbalanced design also made it impossible to test for an interaction between filter condition and ventilation rate. These results indicate the importance of improving indoor air quality and ventilation in classrooms.